MicroRNA expression classification for pediatric multiple sclerosis identification

Abstract

MicroRNAs (miRNAs) are a set of short non-coding RNAs that play significant regulatory roles in cells. The study of miRNA data produced by Next-Generation Sequencing techniques can be of valid help for the analysis of multifactorial diseases, such as Multiple Sclerosis (MS). Although extensive studies have been conducted on young adults affected by MS, very little work has been done to investigate the pathogenic mechanisms in pediatric patients, and none from a machine learning perspective. In this work, we report the experimental results of a classification study aimed at evaluating the effectiveness of machine learning methods in automatically distinguishing pediatric MS from healthy children, based on their miRNA expression profiles. Additionally, since Attention Deficit Hyperactivity Disorder (ADHD) shares some cognitive impairments with pediatric MS, we also included patients affected by ADHD in our study. Encouraging results were obtained with an artificial neural network model based on a set of features automatically selected by feature selection algorithms. The results obtained show that models developed on automatically selected features overcome models based on a set of features selected by human experts. Developing an automatic predictive model can support clinicians in early MS diagnosis and provide new insights that can help find novel molecular pathways involved in MS disease.

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

“…The study achieved significant results, but the limitation of the study was that the dataset was not large. Additionally, Casino et al [ 74 ] proposed a model that could discriminate between MS and ADHA. The diseases share similarities, and therefore, it is significant to develop a model that can discriminate between them.…”

“…Some studies have used DL techniques for the diagnosis of MS using clinical data or human activity data collected via several sensors. Casalino et al [ 74 ] developed a multi-class classification model that discriminates between ADHD and pediatric MS using miRNA expressions. They experimented with RF, extremely randomized trees, and multi-layer perceptron (MLP).…”

Fuzzy Logic System for Classifying Multiple Sclerosis Patients as High, Medium, or Low Responders to Interferon-Beta

“…. + w m x m , followed by a non-linear activation function that is used to learn the weights, and then the output layer that predicts the class label of the samples [32]. During the learning stage, MLP compares the true class labels to the continuous output values of the nonlinear activation function, to compute the prediction error and update the weights.…”

MAFUS: a Framework to predict mortality risk in MAFLD subjects

“…In recent years, due to the increasing prevalence of NAFLD and a new definition of MAFLD, there is a research trend toward identifying low-cost diagnostic methods, and ML is acknowledged as a valuable method. In clinical practice, numerous works have shown how ML or e-health tools are considered various alternatives to standard diagnostic methods [10][11][12][13] such as Magnetic Resonance Imaging (MRI), ultrasounds, etc. ML approaches are already used for NAFLD diagnosis [14,15].…”